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MCP server for AI-SEO auditing: schema, robots.txt, llms.txt, citation scoring, and AEO/GEO rewrites
MCP server for AI-SEO auditing: schema, robots.txt, llms.txt, citation scoring, and AEO/GEO rewrites
Valid MCP server (2 strong, 4 medium validity signals). No known CVEs in dependencies. Package registry verified. Imported from the Official MCP Registry.
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Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-automatelab-tech-ai-seo": {
"args": [
"-y",
"@automatelab/ai-seo-mcp"
],
"command": "npx"
}
}
}From the project's GitHub README.
Vendor-agnostic MCP server that audits, scores, and rewrites web pages for AI-citation eligibility. No API keys. No registration.
Works in Claude Desktop, Cursor, Cline, Windsurf, VS Code (Copilot / Continue), and any client that speaks the Model Context Protocol.
AI assistants are eating search. ChatGPT, Perplexity, Google AI Overviews, and Claude with web access answer a fast-growing share of "what is X" and "how do I Y" queries - and each answer cites a handful of pages. Being one of those cited sources is binary: you are in the answer, or you are invisible.
The signals that decide citation eligibility are not what classic SEO tools measure. Lighthouse will not flag missing FAQPage schema. Search Console will not tell you that GPTBot is allowed but OAI-SearchBot is blocked. Ahrefs does not score citation worthiness. Screaming Frog does not validate llms.txt.
A page that no AI assistant cites is a page that, for a growing share of users, does not exist.
This MCP gives you a short list of no-brainer fixes that dramatically increase the odds of getting cited by an AI assistant versus unprepared text. It audits the specific signals AI assistants use - schema completeness, FAQ structure, AI-crawler allowlists, llms.txt, entity density, freshness, authority - and for each gap it returns the exact change to make. No opaque scores, no guesswork. Most of the fixes are one-time edits to a template or a robots.txt and pay off on every page you publish from then on.
Modern search increasingly happens inside AI assistants. ChatGPT Search, Perplexity, Google AI Overviews, Claude with web access, and Microsoft Copilot all cite pages they consider authoritative, well-structured, and machine-readable. The signals that drive those citations overlap with classic SEO but are not the same.
This MCP gives any AI client a toolkit to inspect a URL and answer questions like:
robots.txt?13 tools. Deterministic where possible. Rule-based scoring with explicit rubrics, not black-box numbers.
npx -y @automatelab/ai-seo-mcp
Requires Node 20 or later. No global install needed.
Add to %APPDATA%\Claude\claude_desktop_config.json on Windows, or ~/Library/Application Support/Claude/claude_desktop_config.json on macOS:
{
"mcpServers": {
"ai-seo": {
"command": "npx",
"args": ["-y", "@automatelab/ai-seo-mcp"]
}
}
}
Restart Claude Desktop. The 13 tools appear in the tool tray.
Add to .cursor/mcp.json in the project root or ~/.cursor/mcp.json globally:
{
"mcpServers": {
"ai-seo": {
"command": "npx",
"args": ["-y", "@automatelab/ai-seo-mcp"]
}
}
}
Add to VS Code settings or .cline/mcp_settings.json:
{
"mcpServers": {
"ai-seo": {
"command": "npx",
"args": ["-y", "@automatelab/ai-seo-mcp"],
"disabled": false,
"autoApprove": []
}
}
}
Use the same command / args pattern. Any MCP client that supports stdio transport works.
After adding the config snippet above and restarting your client, try these prompts:
https://example.com and tell me the top three things to fix." - calls audit_page and returns a scored report with prioritized findings.robots.txt for https://yourdomain.com." - calls check_robots and lists per-crawler allow/disallow.https://...." - calls score_citation_worthiness.llms.txt for https://yourdomain.com from its sitemap." - calls generate_llms_txt.rewrite_for_aeo (uses MCP sampling if your client supports it).No API keys. No accounts. The first call may take a few seconds while npx downloads the package; subsequent calls are instant.
| Tool | Purpose |
|---|---|
audit_page | Composite AI-SEO audit with 8-dimension scoring (schema, technical, structure, robots, freshness, authority, entity density, sitemap). |
audit_schema | Validate JSON-LD against Schema.org rules and AI-citation best practice. Flags deprecated patterns. |
audit_canonical | Canonical link integrity, trailing-slash hygiene, og:url consistency. |
check_robots | Parse robots.txt and report per-crawler allow/disallow for all known AI crawlers. Surfaces the GPTBot-blocked-but-OAI-SearchBot-allowed trap. |
check_sitemap | Validate XML sitemaps: presence, URL count, lastmod freshness, image/video extensions. |
check_technical | HEAD tag audit: canonical, OpenGraph, Twitter Card, hreflang, HTTPS, noindex, title hygiene. |
score_ai_overview_eligibility | Score a page's probability of appearing in Google AI Overviews using current correlation factors. |
generate_llms_txt | Generate llms.txt and optionally llms-full.txt from a domain's sitemap. |
validate_llms_txt | Lint an existing llms.txt for spec compliance and broken links. |
score_citation_worthiness | Score how citable a page or text block is for Perplexity, ChatGPT, Google AI Overviews, and Claude. |
rewrite_for_aeo | Rewrite content for Answer Engine Optimization (BLUF structure, FAQ format, schema additions). |
rewrite_for_geo | Rewrite content for Generative Engine Optimization (entity definitions, comparison tables, synthesis-ready structure). |
extract_entities | Extract named entities, sameAs links, and citation-density score from a page's content and structured data. |
In Claude Desktop, after wiring the server above:
You: Run an AI-SEO audit on
https://automatelab.tech/how-to-connect-zapier-to-notion.
Claude calls audit_page. Result (truncated):
{
"url": "https://automatelab.tech/how-to-connect-zapier-to-notion",
"fetched_at": "2026-05-15T10:32:00Z",
"score": 61,
"grade": "C",
"dimension_scores": {
"schema": 45, "technical": 80, "structure": 40,
"robots": 90, "freshness": 85, "authority": 40,
"entity_density": 21, "sitemap": 100
},
"findings": [
{
"severity": "critical",
"category": "structure",
"where": "<body>",
"message": "No FAQ structure found (no FAQPage schema or H3 question headings).",
"fix": "Add FAQ H3 headings ending in '?' with answer paragraphs, and a FAQPage JSON-LD block.",
"estimated_impact": "high"
},
{
"severity": "warning",
"category": "authority",
"where": "page-level",
"message": "Low authority signals - missing Organization or author Person schema.",
"fix": "Add Organization JSON-LD and Article.author as a Person node with sameAs links.",
"estimated_impact": "high"
}
]
}
Claude then summarizes the findings and proposes fixes. For rewrite tools, the host model (Claude / GPT / etc.) does the rewriting via MCP sampling.
All variables are optional. Set them in the MCP client config under "env".
| Variable | Default | Description |
|---|---|---|
USER_AGENT | automatelab-ai-seo-mcp/0.1.0 (+https://github.com/AutomateLab-tech/ai-seo) | HTTP User-Agent on all fetches. |
FETCH_TIMEOUT_MS | 15000 | Per-request timeout in milliseconds. |
MAX_BYTES | 5242880 | Maximum response body size in bytes (5 MB). |
RESPECT_ROBOTS | true | Global default for robots.txt compliance. Set "false" to disable. |
INTER_REQUEST_DELAY_MS | 1500 | Minimum delay between requests to the same host within a tool call. |
Every tool that hits the network goes through one fetch path that:
robots.txt for the configured User-Agent (override with RESPECT_ROBOTS=false).User-Agent (no spoofing as a browser).INTER_REQUEST_DELAY_MS between requests to the same host within a tool call.FETCH_TIMEOUT_MS per request.MAX_BYTES and refuses non-HTML / non-XML payloads where inappropriate.This MCP is not a scraper. It audits public pages the way Lighthouse or Screaming Frog do, one URL at a time.
Bug reports, feature ideas, and PRs welcome. See CONTRIBUTING.md.
To report a vulnerability, see SECURITY.md.
MIT - see LICENSE.
Built by automatelab.tech
Schema.org, robots.txt, and llms.txt are open standards. Crawler user-agent data is sourced from Anthropic, OpenAI, Google, Perplexity, and Cloudflare documentation. The MCP itself is built on the Model Context Protocol by Anthropic.
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